Environment Variables

import os

from fastapi import FastAPI
import mlfoundry as mlf
client = mlf.get_client()

# NOTE: `MODEL_FQN` variable will now contain the value
# of environment variable `MODEL_FQN`
MODEL_FQN = os.getenv("MODEL_FQN")
MODEL = client.get_model(MODEL_FQN).load()

S3_BUCKET = os.getenv("S3_BUCKET_NAME")

app = FastAPI()


@app.get("/infer")
def infer(...):
    ...

MODEL_FQN="YOUR MODEL FQN" S3_BUCKET_NAME="my-s3-bucket" uvicorn main:app --port 8000 --host 0.0.0.0

"""
Both `Service ` and `Job` classes have an argument `env` where you can pass a dictionary. The dictionary keys will be assumed as environment variable names and the values will be the environment variable values.
"""

import logging

from servicefoundry import Build, Service, DockerFileBuild

logging.basicConfig(level=logging.INFO)
service = Service(
    name="my-service",
    image=Build(build_spec=DockerFileBuild()),
    ports=[{"port": 8501}],
    env={
      "MODEL_FQN": "YOUR MODEL FQN",
      "S3_BUCKET_NAME": "my-s3-bucket"
    },
)
service.deploy(workspace_fqn="YOUR_WORKSPACE_FQN")

# You can add your environment variables under `env:` section as key value pairs.

name: my-service
components:
  - name: my-service
    type: service
    image:
      type: build
      build_source:
        type: local
      build_spec:
        type: dockerfile
    ports:
     - port: 8501
    env:
      MODEL_FQN: "YOUR MODEL FQN"
      S3_BUCKET_NAME: my-s3-bucket